التفاصيل البيبلوغرافية
| العنوان: |
Artificial Neural Networks for Mineral Production Forecasting in the In Situ Leaching Process: Uranium Case Study |
| المؤلفون: |
Daniar Aizhulov, Madina Tungatarova, Maksat Kurmanseiit, Nurlan Shayakhmetov |
| المصدر: |
Processes ; Volume 12 ; Issue 10 ; Pages: 2285 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2024 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
in situ leaching, mineral extraction, neural networks, production forecasting, machine learning, artificial intelligence |
| جغرافية الموضوع: |
agris |
| الوصف: |
This study was conducted to assess the applicability of artificial neural networks (ANN) for forecasting the dynamics of uranium extraction over exploitation time during the process of In Situ Leaching (ISL). Currently, ISL process simulation involves multiple steps, starting with geostatistical interpolation, followed by computational fluid dynamics (CFD) and reactive transport simulation. While extensive research exists detailing each of these steps, machine learning techniques may offer the potential to directly obtain extraction curves (i.e., the concentration of the mineral produced over the exploitation time of the deposit), thereby bypassing these computationally expensive steps. As a basis, both an empirical experimental configuration and reactive transport simulations were used to generate training data for the neural network model. An ANN was constructed, trained, and tested on several test cases with different initial parameters, then the expected outcomes were compared to those derived from conventional modeling techniques. The results indicate that for the employed experimental configuration and a limited number of features, artificial intelligence technologies, specifically regression-based neural networks can model the recovery rate (or extraction degree) of the ISL process for mineral production, achieving a high degree of accuracy compared to traditional CFD and mass transport models. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Energy Systems; https://dx.doi.org/10.3390/pr12102285 |
| DOI: |
10.3390/pr12102285 |
| الاتاحة: |
https://doi.org/10.3390/pr12102285 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.DB07A2EF |
| قاعدة البيانات: |
BASE |